9 repositorios
Evaluates equality between scalar or vector types using custom tolerance values to handle floating-point precision errors.
Distinct from Value Comparators: Distinct from general value comparators: focuses on epsilon-based tolerance for floating-point equality testing.
Explore 9 awesome GitHub repositories matching data & databases · Epsilon-Based Type Comparators. Refine with filters or upvote what's useful.
This project is a header-only C++ library designed for graphics mathematics, providing a comprehensive suite of vector, matrix, and quaternion types. It is built using template metaprogramming to generate mathematical primitives at compile time, eliminating the need for precompiled binary libraries and allowing for direct integration into existing build systems. The library is distinguished by its strict adherence to the OpenGL Shading Language specification, ensuring that mathematical results remain consistent across both CPU and GPU code. It provides specialized utilities for managing float
Evaluates equality between scalar or vector types using custom tolerance values to ensure accurate results.
go-swagger is a toolkit for working with Swagger/OpenAPI 2.0 specifications in Go. It generates server, client, and CLI code from a specification document, and can also produce a specification by scanning annotated Go source code. The project includes a static validation engine that checks documents against the schema and project-specific rules, and a specification transformation pipeline that resolves, flattens, and merges documents. The toolkit generates both client and server code from the same specification, ensuring consistency in request and response handling. It also produces a command
Compares two Swagger specification documents and reports breaking changes in backwards compatibility.
100 Go Mistakes is a reference book and code review companion that catalogues frequent Go programming anti-patterns and provides corrected implementations for each one. It covers a wide range of common pitfalls, from range loop variable capture and interface nil handling to error wrapping and map iteration randomization, helping developers recognize and avoid these issues in their own code. The project distinguishes itself by offering a structured, example-driven approach to learning idiomatic Go. It covers core design decisions such as when to use pointer versus value receivers, how to apply
Teaches correct comparison patterns for Go's comparable and non-comparable types.
This library is a data assertion tool and equality logic framework for PHP. It provides utilities to verify that two values, nested objects, or complex data types match based on their internal contents. The project distinguishes itself through the use of custom matching rules and configurable precision. It allows for the comparison of floating point numbers and dates using a defined margin of error to account for numeric precision loss. The framework covers deep value equality verification across scalars, arrays, and nested objects. It implements strict type enforcement to prevent implicit c
Allows comparison of complex types, such as dates and numbers, using specified tolerance levels to determine equality.
attrs is a Python library that automatically generates initialization, representation, equality, hashing, and ordering methods from declarative class attribute definitions. At its core, it provides a class decorator metaprogramming framework that intercepts class creation to rewrite the class body, producing dunder methods without manual boilerplate. The library includes a comprehensive attribute validation toolkit with built-in validators for type checks, range constraints, regex matching, length limits, and logical composition of validation rules. The library distinguishes itself through it
Uses user-supplied callables to define how fields containing special types like NumPy arrays are compared.
Unity es un framework de pruebas unitarias ligero para C, que proporciona la biblioteca de aserciones, el ejecutor de pruebas y los mecanismos de reporte necesarios para verificar la corrección del código. Funciona como infraestructura de pruebas central para organizar y ejecutar pruebas unitarias en entornos C. El framework está diseñado para la validación de software a nivel de sistema y embebido, con capacidades específicas para verificar firmware y controladores de hardware. Se centra en la integridad de la memoria y la validez de los punteros, permitiendo la validación de estados de punteros y la inspección de bloques de memoria crudos. El conjunto de herramientas cubre una amplia gama de tipos de comparación, incluyendo verificación numérica para enteros, patrones de bits y valores de punto flotante. También proporciona utilidades para validar cadenas terminadas en nulo, contenidos de arrays y lógica booleana, mientras soporta mensajes de error personalizados para proporcionar contexto durante la depuración.
Provides epsilon-based comparisons to handle floating-point precision errors during test assertions.
Este proyecto es una traducción al chino de una guía completa sobre el lenguaje de programación Go. Sirve como un recurso educativo localizado y un manual técnico diseñado para proporcionar orientación sobre la sintaxis del lenguaje, su diseño y el desarrollo de software. El recurso cubre una amplia gama de educación sobre el lenguaje Go, incluida la implementación de patrones de programación y diseño de sistemas. Incluye lecciones traducidas y ejemplos que se centran en características principales del lenguaje como la concurrencia y el uso de interfaces. El contenido abarca varias áreas de capacidad, incluidos los fundamentos del lenguaje, modelado de datos, reflexión en tiempo de ejecución y gestión de memoria. También proporciona una cobertura detallada de la arquitectura de software, manejo de errores, control de calidad y redes web. La documentación está estructurada como un manual técnico que incluye contenido traducido, erratas y correcciones para garantizar un aprendizaje preciso.
Details how Go compares complex data structures and determines if they are identical.
Dedupe is a machine learning tool for entity resolution that identifies and merges duplicate records in structured datasets. It uses active learning to train a matching model from human-labeled examples, learning which field-level similarities are most important for detecting duplicates without requiring manual rule writing. The system combines fingerprint-based blocking to reduce pairwise comparisons, enabling efficient matching on large datasets, and groups scored record pairs into clusters using a configurable similarity threshold. The tool provides multiple interfaces for different workfl
Supports adding custom data types, string comparators, and blocking rules for domain-specific matching.
This project is a C++ learning resource and study guide consisting of structured notes and programming examples. It provides practical implementations and exercise solutions covering core language syntax, data types, and control flow. The repository features specialized samples for object-oriented design, including class inheritance, polymorphism, and abstract classes. It includes demonstrations of memory management techniques such as dynamic allocation, move semantics, and placement new, as well as template programming examples for creating generic functions and data structures. The codebas
Implements custom string comparison functions to trigger program behavior or count words.